Comparing the Performance of Data Mining Algorithms in Predicting Sentiments on Twitter

Comparing the Performance of Data Mining Algorithms in Predicting Sentiments on Twitter

sentiment analysis; twitter; SVM; K-NN

On the social networking site Twitter, users can post tweets, videos, and images. It can, however, also be disruptive and difficult.
In order to categorize material and improve searchability, hashtags are crucial. This study focuses on examining the opinions
of Twitter users who participate in trending topics. The algorithms K-Nearest Neighbor (KNN) and Support Vector Machine
(SVM) are employed for sentiment analysis. The dataset comprises of tweet information on popular subjects that was collected
using the Twitter API and saved in Excel format. SVM and K-NN are used for data preparation, weighting, and sentiment
analysis. With 105 data points, the study provides insights into user sentiment. SVM identified 99% of positive and 1% of
negative replies with accuracy of 80%. KNN successfully identified 90% of positive and 10% of negative responses, with an
accuracy rate of 71.4%. According to the results, SVM performs better when analyzing the sentiment of hashtag users on
Twitter

Rusydi Umar, Sunardi, Muhammad Nur Ardhiansyah

http://jurnal.iaii.or.id

Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association

August 2023

Sri Wahyuni

ISSN Media Electronic: 2580-0760

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